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  • Received: 12 September 2017 | Revised: 16 December 2017 | Accepted: 14 January 2018 DOI: 10.1002/ajp.22743


    Non-invasive genetic censusing and monitoring of primate populations

    Mimi Arandjelovic | Linda Vigilant

    Department of Primatology, Max Planck

    Institute for Evolutionary Anthropology,

    Leipzig, Germany


    Mimi Arandjelovic, Department of

    Primatology, Max Planck Institute for

    Evolutionary Anthropology, Deutscher Platz 6,

    04103, Leipzig, Germany

    Email: [email protected]

    Funding information


    Knowing the density or abundance of primate populations is essential for their

    conservation management and contextualizing socio-demographic and behavioral

    observations. When direct counts of animals are not possible, genetic analysis of non-

    invasive samples collected from wildlife populations allows estimates of population

    size with higher accuracy and precision than is possible using indirect signs.

    Furthermore, in contrast to traditional indirect survey methods, prolonged or periodic

    genetic sampling across months or years enables inference of group membership,

    movement, dynamics, and some kin relationships. Data may also be used to estimate

    sex ratios, sex differences in dispersal distances, and detect gene flow among

    locations. Recent advances in capture-recapture models have further improved the

    precision of population estimates derived from non-invasive samples. Simulations

    using these methods have shown that the confidence interval of point estimates

    includes the true population size when assumptions of the models are met, and

    therefore this range of population size minima and maxima should be emphasized

    in population monitoring studies. Innovations such as the use of sniffer dogs or

    anti-poaching patrols for sample collection are important to ensure adequate

    sampling, and the expected development of efficient and cost-effective genotyping

    by sequencing methods for DNAs derived from non-invasive samples will automate

    and speed analyses.


    capture-recapture, conservation genetics, feces, mark-recapture, population estimation,

    wildlife monitoring


    Primate populations in the wild are increasingly threatened by

    habitat loss and fragmentation (Bergl, Bradley, Nsubuga, & Vigilant,

    2008; Campbell, Kuehl, N'Goran Kouamé, & Boesch, 2008; Junker

    et al., 2012; Roos et al., 2014; Schwitzer et al., 2014; Vijay, Pimm,

    Jenkins, & Smith, 2016; Wich et al., 2008), illegal activities (Plumptre

    et al., 2016; Ripple et al., 2016; Svensson et al., 2016), and disease

    (Leendertz et al., 2006; Rudicell et al., 2011; Walsh et al., 2003).

    The majority of primate species are thought to be declining in number

    and threatened with extinction (Estrada et al., 2017). Knowledge of

    population numbers and their distribution over time is essential for

    designing and assessing effective conservation measures as well as

    evaluating the level of threat for a given taxon. Particularly helpful is

    additional information on membership of individuals in groups and

    how dispersal by individuals, and consequent gene flow, occurs or is

    impeded across disrupted or fragmented landscapes. Furthermore,

    many demographic processes such as ranging, feeding and association

    patterns are dependent on population density, making these estimates

    critical to studying primate sociobiology.

    Am J Primatol. 2018;e22743. © 2018 Wiley Periodicals, Inc. | 1 of 14

  • Traditional approaches for estimating the abundance of primate

    populations typically rely upon the systematic assessment of the

    number and distribution of direct, and often indirect, signs such as

    nests, vocalizations, or feeding remains (Kühl, Maisels, Ancrenaz, &

    Williamson, 2008; Plumptre, Sterling, & Buckland, 2013). Obtaining

    direct counts of primates for population estimates is often challenging

    due to the lowdensity and elusive behavior of individuals aswell as low

    visibility in forested habitats.When direct counts are possible, distance

    sampling methods are often employed and estimation of detection

    probabilities based on visibility and proximity to the observer are

    required to extrapolate observations into population size estimates

    (Plumptre et al., 2013), which may vary from observer to observer

    (Mitani, Struhsaker, & Lwanga, 2000). Furthermore, when indirect

    signs such as nest counts are utilized, transforming these data into

    abundance estimates requires knowledge of local accumulation and

    decay rates, which may vary temporally and among locations and can

    be time-consuming to estimate (Kouakou, Boesch, & Kuehl, 2009;

    Todd, Kuehl, Cipolletta, & Walsh, 2008). These methodological issues

    have likely contributed to the high variability of reported primate

    densities across sites and habitat types (Buckland, Plumptre, Thomas,

    & Rexstad, 2010; Devos et al., 2008). Furthermore, as estimates based

    on the aforementioned assumptions and extrapolations tend to suffer

    from low precision, comparisons over time to monitor population size

    changes are highly problematic (e.g., Roy et al., 2014).

    In recent years, genetic approaches for estimating population sizes

    have been used as a complement or alternative to nest count surveys in

    great apes. “Genetic censusing” may refer to any approach whereby

    DNAanalysis serves to reliably attribute samples to different individuals

    to achieve an estimate of population abundance or density. To avoid the

    expense, danger, and behavioral impact of trapping or darting animals,

    genetic censusing studies largely rely upon non-invasively collected

    samples such as scats or hair samples. Although the use of non-invasive

    samples as a means toward population assessments was already

    demonstrated some 20 years ago (Kohn &Wayne, 1997; Taberlet et al.,

    1997), it is only in recent years that theapproachhasbeenadoptedmore

    widely, especially in great apes. To date few studies have utilized this

    method in other primates, although its potential for primate surveys is

    substantial (Chang, Liu, Yang, Li, & Vigilant, 2012;Orkin, Yang, Yang, Yu,

    & Jiang, 2016). Here we provide an overview of how non-invasive

    genetic censusingmethods have been used in the primatology field thus

    far, highlighting advances in analysis techniques, the wealth of extra

    information that can be extracted from genetic census datasets, and

    study design considerations for researchers embarking on their own

    genetic censusing project.


    2.1 | Minimum count estimates

    A genetic census will always produce a count of distinct genotypes,

    which can serve as a minimum estimate of the number of individuals in

    a given area. Such minimum count estimates may be the by-product of

    research aimed at other questions. For example, in a survey of simian

    immunodeficiency virus (SIV) prevalence in a low density, savanna-

    dwelling eastern chimpanzee population, genotyping of nearly 400

    samples resulted in the identification (and therefore minimum count

    estimate) of 72 individuals in the area (Rudicell et al., 2011). However,

    genotype data do not necessarily allow further inference of the

    number of undetected individuals and, therefore, the estimation of

    population size. For example, the first attempt to incorporate genetic

    analysis into a mountain gorilla population survey included microsat-

    ellite genotyping of 384 samples of the Bwindi mountain gorilla

    population,which yielded 354 genotyped samples thatwere attributed

    to 257 individuals (Guschanski et al., 2009). The genetic data further

    documented that individual gorillas may make more than one night

    nest, and sets of nearby nests may be misattributed to groups,

    emphasizing the limitations of indirect signs alone as a means of

    censusing gorillas. Despite the large number of samples analyzed,

    the collection of all samples in just one field session excluded the

    possibility of independent resampling, which is necessary to evaluate

    the capture frequency of individuals and estimate the number of

    undetected individuals.

    2.2 | Genetic capture-recapture population size estimation

    More informative than a single sampling session are repeated visits to

    an area for sample collection over a prolonged period, allowing for the

    use of genetic capture-recapture (CR) population size estimators

    (Miller, Joyce, & Waits, 2005; Pennell, Stansbury, Waits, & Miller,

    2013; Petit & Valière, 2006; Schwartz, Luikart, &Waples, 2007; Waits

    & Paetkau, 2005). Instead of tagging actual individuals, genetic CR

    studies build on the long history of use of capture-mark-recapture

    (CMR) in wildlife studies (Seber, 1982) by relying upon repeated

    “tagging” of an individual's DNA, such as through use of genetic